chore: import upstream snapshot with attribution

python/paddle/jit/sot/utils/paddle_api_config.py

@@ -0,0 +1,168 @@
+# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import inspect
+
+import paddle
+
+
+def is_inplace_api(func):
+    inplace_apis = {paddle.static.setitem}
+    return func in inplace_apis
+
+
+def get_tensor_methods():
+    return [
+        member_name
+        for member_name, member in inspect.getmembers(paddle.pir.Value)
+        if inspect.isfunction(member) or inspect.ismethoddescriptor(member)
+    ]
+
+
+def get_paddle_api():
+    modules = [
+        paddle,
+        paddle.nn.functional,
+        paddle.nn.quant,
+        paddle.incubate.nn.functional,
+        paddle.linalg,
+        paddle.signal,
+        paddle.fft,
+        paddle.vision.ops,
+        paddle.metric,
+        paddle.geometric,
+    ]
+    distributed_apis = [
+        paddle.distributed.all_reduce,
+        paddle.distributed.shard_tensor,
+        paddle.distributed.reshard,
+        paddle.distributed.all_gather,
+        paddle.distributed.alltoall,
+        paddle.distributed.barrier,
+        paddle.distributed.recv,
+        paddle.distributed.send,
+        paddle.distributed.broadcast,
+        paddle.distributed.unshard_dtensor,
+        paddle.distributed.auto_parallel.api.dtensor_to_local,
+        paddle.distributed.auto_parallel.api.dtensor_from_local,
+        paddle.distributed.auto_parallel.api.moe_global_mesh_tensor,
+        paddle.distributed.auto_parallel.api.moe_sub_mesh_tensors,
+    ]
+    special_paddle_apis = [
+        paddle.tensor.fill_constant,
+        paddle.tensor.top_p_sampling,
+    ]
+    non_operator_related_apis = [
+        paddle.in_dynamic_mode,
+        paddle.save,
+        paddle.load,
+        paddle.get_cuda_rng_state,
+        paddle.set_rng_state,
+        paddle.set_cuda_rng_state,
+        paddle.get_rng_state,
+        paddle.set_default_dtype,
+        paddle.check_shape,
+        paddle.summary,
+        paddle.finfo,
+        paddle.iinfo,
+        paddle.enable_static,
+        paddle.disable_static,
+        paddle.is_grad_enabled,
+    ]
+    # TODO: users should not call static_apis, but we need to use, so add static_apis here temporary
+    static_apis = [paddle.static.setitem, paddle.static.accuracy]
+    paddle_api_list = []
+    for module in modules:
+        for fn_name in getattr(module, "__all__", []):
+            fn = getattr(module, fn_name)
+            if inspect.isfunction(fn):
+                paddle_api_list.append(fn)
+    return list(
+        set(special_paddle_apis)
+        | set(distributed_apis)
+        | set(static_apis)
+        | set(paddle_api_list) - set(non_operator_related_apis)
+    )
+
+
+paddle_api_list = get_paddle_api()
+
+# TODO(Aurelius84): It seems that we use it to judge 'in_paddle_module()'.
+# Bug what does 'is_paddle_module' really means? Is all paddle.xx sub module
+# considered as paddle module？
+paddle_api_module_prefix = {
+    "paddle.nn.functional",
+}
+
+break_graph_functions = set()
+break_graph_layer_classes = set()
+break_graph_tensor_method = {
+    'register_hook',
+    'numpy',
+    'clear_gradient',
+    'tolist',
+    'item',
+    # TODO: Browse all possible functions and make prior judgments.
+}
+
+not_supported_paddle_layer = {paddle.nn.RNN}
+
+
+def is_not_supported_paddle_layer(layer_class):
+    return layer_class in not_supported_paddle_layer
+
+
+def is_break_graph_tensor_methods(method_name):
+    return method_name in break_graph_tensor_method
+
+
+def add_break_graph_function(fn):
+    break_graph_functions.add(fn)
+
+
+def add_break_graph_layer_class(layer_class: type[paddle.nn.Layer]):
+    break_graph_layer_classes.add(layer_class)
+
+
+def is_directly_run_api(api):
+    from .utils import hashable
+
+    if not hashable(api):
+        return False
+    NATIVE_CODE_PURE_FUNCTIONS = {
+        paddle.base.libpaddle.is_compiled_with_avx,
+        paddle.base.libpaddle.is_compiled_with_cuda,
+        paddle.base.libpaddle.is_compiled_with_cudnn_frontend,
+        paddle.base.libpaddle.is_compiled_with_rocm,
+        paddle.base.libpaddle.is_compiled_with_custom_device,
+        paddle.base.libpaddle.is_compiled_with_ipu,
+        paddle.base.libpaddle.is_compiled_with_xpu,
+        paddle.base.libpaddle.is_compiled_with_mkldnn,
+        paddle.base.libpaddle.is_compiled_with_onednn,
+        paddle.base.libpaddle.is_compiled_with_nccl,
+        paddle.base.libpaddle.is_compiled_with_mpi,
+        paddle.base.libpaddle.is_compiled_with_mpi_aware,
+        paddle.base.libpaddle.is_compiled_with_cinn,
+        paddle.base.libpaddle.is_compiled_with_distribute,
+        paddle.base.libpaddle.is_compiled_with_brpc,
+        paddle.base.libpaddle.is_compiled_with_dist,
+        paddle.base.libpaddle.is_compiled_with_flagcx,
+    }
+
+    if hasattr(paddle.base.libpaddle, "get_device_properties"):
+        NATIVE_CODE_PURE_FUNCTIONS.add(
+            paddle.base.libpaddle.get_device_properties
+        )
+
+    return api in NATIVE_CODE_PURE_FUNCTIONS